IBM Data Science Practice Test

In data science methodology, which stages are essential after data preparation?

1. Visualize data and transform structured data

2. Train and deploy data models

3. Decide on classification or regression

4. Transform data and normalize consistently

The correct answer is: Train and deploy data models

After data preparation, the essential stages in data science methodology focus on the application of analytical techniques to extract insights and make predictions. Training and deploying data models directly follows the data preparation phase, as this step involves utilizing the clean and transformed data to build predictive models. In this context, training refers to the process where algorithms learn patterns from the prepared data through various techniques, helping to create a robust model that can make accurate predictions. Once a suitable model is trained, deployment is crucial as it involves integrating the model into a production environment where it can provide predictions on new data, making it applicable in real-world scenarios. This stage is critical in the data science lifecycle since the effectiveness of the model can only be realized when it is operationalized and used to drive decisions, automate processes, or enhance applications. Consequently, the focus on model training and deployment aligns with the practical application of data science principles, emphasizing the importance of turning prepared data into actionable results. The other options, while relevant to specific aspects of data science, do not encompass the primary objectives following data preparation as thoroughly as the choice related to training and deploying data models.